{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T10:51:34Z","timestamp":1769165494218,"version":"3.49.0"},"reference-count":31,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,9,23]],"date-time":"2024-09-23T00:00:00Z","timestamp":1727049600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["52265068"],"award-info":[{"award-number":["52265068"]}]},{"name":"National Natural Science Foundation of China","award":["52065022"],"award-info":[{"award-number":["52065022"]}]},{"name":"National Natural Science Foundation of China","award":["20224BAB204050"],"award-info":[{"award-number":["20224BAB204050"]}]},{"name":"National Natural Science Foundation of China","award":["KLCEZ2022-02"],"award-info":[{"award-number":["KLCEZ2022-02"]}]},{"name":"National Natural Science Foundation of China","award":["GJJ2200627"],"award-info":[{"award-number":["GJJ2200627"]}]},{"name":"Natural Science Foundation of Jiangxi Province","award":["52265068"],"award-info":[{"award-number":["52265068"]}]},{"name":"Natural Science Foundation of Jiangxi Province","award":["52065022"],"award-info":[{"award-number":["52065022"]}]},{"name":"Natural Science Foundation of Jiangxi Province","award":["20224BAB204050"],"award-info":[{"award-number":["20224BAB204050"]}]},{"name":"Natural Science Foundation of Jiangxi Province","award":["KLCEZ2022-02"],"award-info":[{"award-number":["KLCEZ2022-02"]}]},{"name":"Natural Science Foundation of Jiangxi Province","award":["GJJ2200627"],"award-info":[{"award-number":["GJJ2200627"]}]},{"name":"Equipment Key Laboratory Project of the Ministry of Education","award":["52265068"],"award-info":[{"award-number":["52265068"]}]},{"name":"Equipment Key Laboratory Project of the Ministry of Education","award":["52065022"],"award-info":[{"award-number":["52065022"]}]},{"name":"Equipment Key Laboratory Project of the Ministry of Education","award":["20224BAB204050"],"award-info":[{"award-number":["20224BAB204050"]}]},{"name":"Equipment Key Laboratory Project of the Ministry of Education","award":["KLCEZ2022-02"],"award-info":[{"award-number":["KLCEZ2022-02"]}]},{"name":"Equipment Key Laboratory Project of the Ministry of Education","award":["GJJ2200627"],"award-info":[{"award-number":["GJJ2200627"]}]},{"name":"Project of Jiangxi Provincial Department of Education","award":["52265068"],"award-info":[{"award-number":["52265068"]}]},{"name":"Project of Jiangxi Provincial Department of Education","award":["52065022"],"award-info":[{"award-number":["52065022"]}]},{"name":"Project of Jiangxi Provincial Department of Education","award":["20224BAB204050"],"award-info":[{"award-number":["20224BAB204050"]}]},{"name":"Project of Jiangxi Provincial Department of Education","award":["KLCEZ2022-02"],"award-info":[{"award-number":["KLCEZ2022-02"]}]},{"name":"Project of Jiangxi Provincial Department of Education","award":["GJJ2200627"],"award-info":[{"award-number":["GJJ2200627"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Electric motors play a crucial role in self-driving vehicles. Therefore, fault diagnosis in motors is important for ensuring the safety and reliability of vehicles. In order to improve fault detection performance, this paper proposes a motor fault diagnosis method based on vibration signals. Firstly, the vibration signals of each operating state of the motor at different frequencies are measured with vibration sensors. Secondly, the characteristic of Gram image coding is used to realize the coding of time domain information, and the one-dimensional vibration signals are transformed into grayscale diagrams to highlight their features. Finally, the lightweight neural network Xception is chosen as the main tool, and the attention mechanism Convolutional Block Attention Module (CBAM) is introduced into the model to enforce the importance of the characteristic information of the motor faults and realize their accurate identification. Xception is a type of convolutional neural network; its lightweight design maintains excellent performance while significantly reducing the model\u2019s order of magnitude. Without affecting the computational complexity and accuracy of the network, the CBAM attention mechanism is added, and Gram\u2019s corner field is combined with the improved lightweight neural network. The experimental results show that this model achieves a better recognition effect and faster iteration speed compared with the traditional Convolutional Neural Network (CNN), ResNet, and Xception networks.<\/jats:p>","DOI":"10.3390\/e26090810","type":"journal-article","created":{"date-parts":[[2024,9,24]],"date-time":"2024-09-24T10:41:47Z","timestamp":1727174507000},"page":"810","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Motor Fault Diagnosis Based on Convolutional Block Attention Module-Xception Lightweight Neural Network"],"prefix":"10.3390","volume":"26","author":[{"given":"Fengyun","family":"Xie","sequence":"first","affiliation":[{"name":"School of Mechanical Electrical and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China"},{"name":"State Key Laboratory of Performance Monitoring Protecting of Rail Transit Infrastructure, East China Jiaotong University, Nanchang 330013, China"},{"name":"Life-Cycle Technology Innovation Center of Intelligent Transportation Equipment, Nanchang 330013, China"}]},{"given":"Qiuyang","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Mechanical Electrical and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China"}]},{"given":"Gang","family":"Li","sequence":"additional","affiliation":[{"name":"School of New Energy, Ningbo University of Technology, Ningbo 315211, China"}]},{"given":"Yang","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mechanical Electrical and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China"}]},{"given":"Enguang","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Mechanical Electrical and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China"}]},{"given":"Shengtong","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Mechanical Electrical and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China"},{"name":"State Key Laboratory of Performance Monitoring Protecting of Rail Transit Infrastructure, East China Jiaotong University, Nanchang 330013, China"},{"name":"Life-Cycle Technology Innovation Center of Intelligent Transportation Equipment, Nanchang 330013, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1080\/01441647.2021.1879310","article-title":"The development of autonomous driving technology: Perspectives from patent citation analysis","volume":"41","author":"Cho","year":"2021","journal-title":"Transp. 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